CanThriftyGene(s)orPredictiveFetalProgrammingfor ...thriftiness of low birth weight individuals [13, 14]. Studies using doubly labeled water have not consis-tently found lower metabolic

Hindawi Publishing CorporationJournal of ObesityVolume 2011, Article ID 861049, 11 pagesdoi:10.1155/2011/861049

Research Article

Can Thrifty Gene(s) or Predictive Fetal Programming forThriftiness Lead to Obesity?

Ulfat Baig,1 Prajakta Belsare,1 Milind Watve,1, 2 and Maithili Jog3

1 Indian Institute of Science Education and Research, Pune 411021, India2 Anujeeva Biosciences Pvt. Ltd., Pune 411030, India3 Department of Biotechnology, Abasaheb Garware College, Pune 411004, India

Correspondence should be addressed to Milind Watve, [email protected]

Received 7 November 2010; Accepted 18 February 2011

Academic Editor: Yvon Chagnon

Copyright © 2011 Ulfat Baig et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Obesity and related disorders are thought to have their roots in metabolic “thriftiness” that evolved to combat periodic starvation.The association of low birth weight with obesity in later life caused a shift in the concept from thrifty gene to thrifty phenotype oranticipatory fetal programming. The assumption of thriftiness is implicit in obesity research. We examine here, with the help of amathematical model, the conditions for evolution of thrifty genes or fetal programming for thriftiness. The model suggests that athrifty gene cannot exist in a stable polymorphic state in a population. The conditions for evolution of thrifty fetal programmingare restricted if the correlation between intrauterine and lifetime conditions is poor. Such a correlation is not observed in naturalcourses of famine. If there is fetal programming for thriftiness, it could have evolved in anticipation of social factors affectingnutrition that can result in a positive correlation.

1. Introduction

Obesity and related disorders are on the rise throughoutthe globe at an alarming rate, the causes of which are notyet completely understood. On the one hand, individualsdiffer substantially in their tendency to accumulate fat,and there are strong familial tendencies suggesting thatgenetic predisposition may play a major role. On theother hand, it is obvious that no alleles can increase infrequency in a population with a rate matching the rateof spread of the obesity epidemic, indicating that geneticsalone does not explain the rise in obesity. The most commonperception, therefore, is that of a interplay between genesand environment. According to the current paradigm, agene or a set of genes predisposing to obesity presumablyevolved owing to a selective advantage in ancestral “feast andfamine” environment and remained in polymorphic state inthe population which is turning pathological in the modernurban environment selectively affecting individuals with thegene(s).

This line of thinking was first stated explicitly by Neel [1]who suggested that a “thrifty” gene helped storage of fat

under conditions of better availability of nutrients andallowed reutilization under starvation. The thrifty gene wasunder positive selection pressure in ancestral life whenseasonal and climatic conditions resulted into fluctuatingfood availability. This concept soon became almost anaxiom, although no such “thrifty” gene or set of genes havebeen convincingly demonstrated. Later, the observation thatindividuals born small for gestational age had a greaterprobability to become obese and type 2 diabetic in laterlife led to the concept of fetal programming [2–4]. Thishypothesis states that if a fetus faces inadequate nutrition inintrauterine life, the body is programmed to be “thrifty” asan adaptation. There are two possible components of thisadaptation. One relates to an immediate gain in terms ofsurvival during fetal and early infant life. The other is apredictive adaptive response in anticipation of starvation inlater life [5]. This distinction is important in understandingthe evolution of fetal programming.

Both the concepts of thrifty gene and thrifty phenotypeby fetal programming have recently faced serious criticismon several grounds [6–11]. The main objections of the criticsare as follows.

2 Journal of Obesity

(1) The original suggestion of Neel [1] was that inindividuals prone to obesity and type 2 diabetes(T2D), a “quick insulin trigger” ensures rapid glucoseuptake which is then converted into fat. In theearly 1960s, it was well known that the levels ofinsulin are higher in prediabetics, and Neel’s originalproposal looked sound. However, insulin resistancewas discovered soon, and it became clear that the“quick insulin trigger” is unlikely to work as believedby Neel [1]. Fat cell-specific insulin receptor knock-outs fail to accumulate fat [12], raising the possibilitythat insulin resistance actually arrests lipogenesis. Itis therefore not logical to call the diabetes-pronegenotypes as “thrifty.”

(2) If thriftiness is due to lower metabolic rate conservingmore energy which gets stored in fat tissue, a lowermetabolic rate should be observed in people havinga predisposition to obesity. Consistent correlationsbetween birth weight and metabolic rate are notfound in empirical data to demonstrate the supposedthriftiness of low birth weight individuals [13, 14].Studies using doubly labeled water have not consis-tently found lower metabolic rates in people withsedentary lifestyle in the modern urban societies [15,16].

(3) Impaired fat oxidation rather than lower metabolicrate appears to be the main contributor to obesityof developmentally stunted individuals [17, 18]. Ifinability to reutilize stored fat is the major causeof obesity, the stored fat is unlikely to help under“famine” conditions, and this is a major blow tothe thriftiness hypotheses. The doubly labeled waterstudies also suggest that obesity is more a product ofhyperphagia than metabolic thriftiness [15, 16]. Theknown genetic mechanisms of obesity also work byinterfering with appetite control rather than throughmetabolic thriftiness [19].

(4) Evidence that obese people have a significantly betterchance of surviving famines is debatable [8]. There-fore, it is doubtful whether obesity actually offeredsufficient advantage during famines to get selected inspite of the fact that obesity is associated with reducedfecundity [20].

(5) Obesity and insulin resistance is associated with anumber of changes in the different body systems andtheir functions as diverse as ovulation, spermato-genesis, innate immunity, wound healing, memory,and cognitive brain functions [10]. The thriftinesshypotheses focus on energy homeostasis alone andoffer no explanation as to why these diverse changesare associated with it.

Realizing the limitations, inadequacies, and flaws of thethrifty gene and fetal programming hypotheses, a number ofalternatives have been suggested.

(a) As alternatives to the thrifty gene hypothesis, (i)Speakman [8] postulated genetic drift rather than selectionfor obesity-related genes. An upper limit on obesity was

set in hunter-gatherer life which was effectively lifted whenhumans became free of predation. Subsequently, the obesity-related genes started spreading by genetic drift. (ii) Corbettet al. [21] argued that today’s obese, diabetic, and PCOS-prone genotype was the ancestral one that had betterfertility in famine conditions. In the modern era of foodsecurity since 1800 AD, an insulin-sensitive genotype thathas better fertility under conditions of food abundancestarted spreading. (iii) Moalem et al. [22] hypothesized thathigh plasma glucose lowers the freezing point of bloodwhich prevents formation of ice crystals in cells throughsupercooling, and this has been suggested as an adaptationto the ice age.

(b) Alternative explanations for fetal programming havealso been offered. (i) Hattersley and Tooke [23] arguedthat the association between low birth weight and insulinresistance arises out of a reverse causation, that is, babieswith insulin resistance genotype are more likely to survivefetal undernourishment. (ii) Wells [11, 24] argued that thereis maternal advantage in fetal programming in the form ofoptimizing maternal inputs per fetus or bet hedging, that is,distribution of risk among offspring.

(c) There are hypotheses that account for genetic as wellas intrauterine effects. (i) Watve and Yajnik [10] arguedthat insulin resistance is a socioecological adaptation thatmediates two phenotypic transitions, namely, (i) transitionin reproductive strategy from “r” (large number of offspringwith little investment in each) to “K” (smaller number ofoffspring with more investment in each) and (ii) transitionfrom “soldier to diplomat,” that is, from a physically aggres-sive behavior to a socially manipulative one. According to thishypothesis, insulin resistance changes the differential budgetallocation to tissues. Since all tissues are not dependenton insulin for nutrient uptake, when insulin resistancedevelops, the uptake of insulin-dependent tissues reduces,and more nutrients become available for insulin independenttissues. Muscles are insulin dependent, and brain is insulinindependent, and, therefore, insulin resistance results indisinvestment from muscles and increased investment inbrain. Insulin resistance is likely to have evolved as a switchin reproductive and sustenance strategies rather than anadaptation to feast and famine. (ii) Extending this logicfurther, Rashidi et al. [25] explained why pancreatic betacells and those of females in particular are more susceptibleto oxidative damage. Under stress conditions, the releaseof stress hormones produces insulin resistance and, owingto reactive oxygen species (ROS) preventing beta cellsfrom secreting insulin at the level required to maintainhomeostasis, diverts glucose to insulin-independent tissues,such as the brain and the fetus. They suggest that pancreaticbeta cells lost part of their antioxidant defense in associationwith brain evolution, and lost even more in females whenplacental mammals evolved.

The emergence of alternative hypotheses having differentimplications for the genetics of obesity has made it evenmore critical that the traditional hypothesis is examinedanalytically. The concept of thriftiness has been mostlydiscussed descriptively and qualitatively and almost never

Journal of Obesity 3

subject to quantitative methods commonly used by evolu-tionary geneticists to test or support any argument. We use asimple mathematical model here to examine the conditionsunder which thrifty gene or fetal programming is likelyto gain a selective advantage and evaluate how likely itis for human ancestors to have evolved constitutive orprogrammable thriftiness. There are no empirical estimatesof the actual fitness contributions of the hypothetical genesor phenotypes, and, therefore, a quantitatively predictivemodel cannot be attempted at this stage. The limitedobjective of our model is to conceptually examine whether athrifty gene or thrifty programming can evolve in principle,whether stable polymorphism in this character is possible,if yes, what are the necessary conditions for its evolutionarystability, and whether the thriftiness hypotheses adequatelyexplain the current obesity epidemic.

2. The Model

To model the possible evolution of thriftiness, we consider3 hypothetical genotypes, namely, a nonthrifty wild typehaving no mechanism for thriftiness (n), a thrifty genotype(tg) which is genetically programmed for thriftiness anda genotype with a capacity for fetal programming forthriftiness (tp). Taking a year as a natural time unit ofseasonality, we assume a simple dichotomy of years withadequate food supply (feast) and those with inadequate foodsupply (famine). Famines are assumed to occur randomlywith a probability pf.

Fitness of an individual with nonthrifty genotype infeast conditions (nf1) is assumed to be greater than that ofindividual with thrifty genotype (tf1) because there is costassociated with thriftiness. When there is a feast, nonthriftyindividuals will do better as they do not pay the cost forbeing thrifty (nf1 > tf1). The cost of thriftiness is justifiablebased on the reproductive effects of obesity. We assumethat thrifty individuals are fast to become obese in feastconditions, and there are multiple mechanisms by whichobesity causes reduction in fecundity. On the one hand,obesity and insulin resistance are associated with ovulationdisorders and are major risk factors for polycystic ovarysyndrome (PCOS) [26]. On the other, reduced fecundityis also seen in obese women with regular cycles [20, 27].Moreover, obesity also affects spermatogenesis in men [29],and the effects of obesity on male and female fertility becomeadditive if a couple is obese [30].

Fitness of an individual with nonthrifty genotype infamine conditions (nf0) is assumed to be less than that ofindividuals with thrifty genotype (tf0) (nf0 < tf0).

The lifetime fitness of an individual is given as follows.For individuals with nonthrifty genotype,

Ln = pf× nf0 + (1− pf)× nf1. (1)For individuals with thrifty genotype,

Ltg = pf× tf0 + (1− pf)× tf1. (2)For individuals with thrifty phenotype or the capacityfor irreversible fetal programming, assuming no correla-tion between birth and lifetime conditions, the total fitness

is calculated as the sum of all years, with the assumptionthat in the birth year, the phenotype is best suited forgiven conditions. For the rest of the lifetime (S), the fitnessfluctuates according to randomly fluctuating environmentalconditions. Therefore,

Ltp = 1S× [pf× tf0 + (1− pf)nf1] + S− 1

S

× {[pf× Ltg] + [(1− pf)× Ln]}.(3)

The first term in (3) denotes fitness contribution of birthyear, and the second term sums the fitness contribution ofthe rest of the lifespan.

Solution: Considering the nonthrifty and thrifty genotypesalone, it can be seen that, at large pf, thrifty gene has anadvantage over nonthrifty gene, and, at small pf, nonthriftygene cannot be invaded by the thrifty one. The transition isat Ln = Ltg and this happens when

pf(1− pf) =

(tf1− nf1)(nf0− tf0) . (4)

It is important to note that the areas of advantage are decidednot by the absolute fitness values but only by the ratios of thedifferences in fitness in feast and famine conditions.

Equation (4) implies that the evolution of thrifty geneneeds a high frequency of famine. Famines with significantmortality occur with a frequency of once in 100–150 years[8]. If surviving famines is the major selective force for thriftygene, with Pf = 0.01, the advantage of thrifty over nonthriftyphenotype in famine conditions should be more than 100-times the relative loss suffered by thrifty gene in feastconditions. If the obesity-induced reduction in fecundity issizable, the advantage of thriftiness in famines should beexceedingly large for thrifty genes to evolve. Such a largeadvantage should be highly evident and easily measurable,but obese people have not been shown to survive faminessignificantly better than lean individuals [8].

Considering competition between nonthrifty and fetalprogramming genotypes, it can be seen that at higher pf andlower S, Ltp > Ln. The transition line resides at Ln = Ltp.

For a given pf, this condition is met at

S =[pf× (tf0− Ltg)] + [(1− pf)× (nf1− Ln)]

pf× (Ln− Ltg) . (5)

Figure 1(a) shows the transition line demarcating the areasof selective advantage of Ln and Ltp when only thesetwo genotypes compete. At very high pf, however, an all-time thrifty genotype might have an advantage over fetalprogramming. Therefore, we need to examine the areas ofadvantage of Ltp and Ltg.

Taking a similar approach as above, Ltg = Ltp when

S =[pf× (tf0− Ltg)] + [(1− pf)× (nf1− Ln)]

(1− pf)× (Ltg− Ln) . (6)

A graph of pf versus S (Figure 1(b)) shows that, at greaterprobability of famine and greater longevity, thrifty gene(s)


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Area of advantage of thrifty genotype

Area of advantage of fetal programmer

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(b)

Figure 1: Parameter areas of advantage: (a) when only nonthrifty genotype and fetal programmer compete, (b) when only thrifty genotypeand fetal programmer compete. For this and all other figures, (tf1− nf1)/(nf0− tf0) is maintained constant at 1.33.

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Figure 2: Parameter areas of advantage when nonthrifty genotype,thrifty genotype, and fetal programmer compete simultaneously.Fetal programming can evolve for species with short lifespan. If thelife span is longer, fetal programming is unlikely to offer selectiveadvantage over thrifty or nonthrifty genotypes except for a specificand very narrow range of pf.

can evolve. The two results together (Figure 2) imply thatat low probabilities of famine, a nonthrifty gene has a netadvantage, and at high pf, thrifty gene would evolve leavinga very narrow area of advantage for fetal programming. Thearea is narrower for long-lived species whereas for short-livedones, the birth year advantage is large enough as compared tolifetime, and, therefore, fetal programming has a much largerwidth of advantage.

The results so far are based on the assumption thatseasonal or annual climatic variations are random with littleor no predictability. We now consider the effects of suchpredictability. If there exists a correlation r between uterineconditions and lifetime conditions, then it follows that thelifetime probability of famine would be higher if birth wasin a famine year. We can, therefore, write the expectedprobability of facing a famine if the birth year was a famineyear as

pfr0 = (1− pf)× r + pf. (7)

The probability of facing a famine if the birth year was a feastyear as

pfr1 = pf− (pf× r), (8)

accordingly,

Ltp =[pf× tf0 + (1− pf)× nf1]

S

+

{(S− 1)× [pf× {(pfr0× tf0) + (1− pfr0)× tf1}] + [(1− pf)× {(pfr1× nf0) + (1− pfr1)× nf1}]}

S.

(9)


Now, the new transition between areas of advantage of nonthrifty and fetal programming (when Ln = Ltp) is obtained at

S =[pf× tf0 + (1− pf)× nf1]

Ln− {[pf× {(pfr0× tf0) + (1− pfr0)× tf1}] + [(1− pf)× {(pfr1× nf0) + (1− pfr1)× nf1}]}

−{[

pf× {(pfr0× tf0) + (1− pfr0)× tf1}] + [(1− pf)× {(pfr1× nf0) + (1− pfr1)× nf1}]}Ln− {[pf× {(pfr0× tf0) + (1− pfr0)× tf1}] + [(1− pf)× {(pfr1× nf0) + (1− pfr1)× nf1}]} .

(10)

Similarly, the transition between areas of advantage of thriftyand fetal programming is obtained at Ltg = Ltp. This condition is satisfied when

S =[pf× tf0 + (1− pf)× nf1]

Ltg− {[pf× {(pfr0× tf0) + (1− pfr0)× tf1}] + [(1− pf)× {(pfr1× nf0) + (1− pfr1)× nf1}]}

−{[

pf× {(pfr0× tf0) + (1− pfr0)× tf1}] + [(1− pf)× {(pfr1× nf0) + (1− pfr1)× nf1}]}Ltg− {[pf× {(pfr0× tf0) + (1− pfr0)× tf1}] + [(1− pf)× {(pfr1× nf0) + (1− pfr1)× nf1}]} .

(11)

Figure 3 shows that as the birth-time and lifetime correlationincreases, the area of advantage of fetal programmer widens.However, in long-lived species, since the advantage to fetalprogrammer in the absence of correlation is very small, evena small negative correlation drives the fetal programmer toextinction (Figure 3(c)). It is most important for the evolu-tion of fetal programming that a positive correlation betweenbirth-time and lifetime conditions exists. The correlationneed not be very high. It can be seen that at r = 0.3, fetalprogrammer has an advantage over a wide range of pf.

Climatic fluctuations from year to year are a complexphenomenon, and since prediction of important climaticfeatures, such as rainfall, has important implications,there have been serious attempts to detect temporalpatterns. However, temporal patterns are of little help inweather prediction since there are no consistent time lapsecorrelations in rainfall or other parameters. Since India hasthe largest population of diabetics and monsoon is the mostimportant determinant of food availability in this region,it would be enlightening to see the patterns in the Indianmonsoon. Based on the public-domain data on Indianmonsoon by the Indian Institute of Tropical Meteorology(ftp://www.tropmet.res.in/pub/data/rain/iitm-subdivrf.txt),we investigated whether birth year and subsequent year’srainfall has any positive correlation in the short or long runfor any of the 30 monsoon subdivisions in India. Table 1shows that rainfall in a given year is not correlated with thatof the subsequent year, subsequent 10 years, or 40 yearscumulative. Over the 30 monsoon subdivisions, only 6 arestatistically significant using individual α level of 0.05, outof which 4 are negative, contrary to the expectation. UsingBonferroni correction for significance level, applicable whena large number of tests are being done together, none of thecorrelations remain significant. As there is no detectablepositive correlation between birth year and lifetime rainfall

conditions, similarly no such correlational patterns in anyother climatic variables are reported, fetal programmingis unlikely to have evolved in anticipation of drought orfamine.

3. Discussion

The model suggests that a thrifty gene or a set of genes canevolve only if the frequency of famines is very high and undersuch conditions the thrifty allele(s) would not exist in astable polymorphic state. Selection for the ability to programthe fetus is likely in a very narrow range of frequency offamines, and at lower or higher frequency of famines, fetalprogramming would not evolve. The parameter space forthe evolution of fetal programming becomes very narrowand highly specific for long-lived species owing to whichevolution of fetal programming for thriftiness is highlyunlikely.

The model results need to be interpreted carefully in thecontext of ancestral human ecology. One of the key questionsis when in human history could selection for thriftiness, ifany, have operated. We will examine three possible scenariosbelow.

(a) Selection during hunting-gathering stage: pale-oarcheological data suggests that chronic starvation wasuncommon during hunter-gatherer stage [31]. Today’shunter-gatherer societies do not seem to suffer starvationmore frequently or more intensively than agricultural soci-eties [9]. Therefore, the assumption that hunter-gatherersocieties suffered frequent starvation is not well supported,but even if we assume hunter-gatherer societies to be proneto feast and famine selection, a number of other questionsremain unanswered. Since hominids were hunter-gatherersfor the most part of human evolutionary history, selectionwould have been prolonged, and we would expect allelesto have reached equilibrium frequencies. The model implies


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(a)

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(b)

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(c)

Figure 3: Parameter areas of advantage of the 3 genotypes when there is a correlation between birth-time and lifetime conditions: (a) r = 0.1,(b) r = 0.2, and (c) r = −0.05. With a small positive correlation, the advantage of fetal programmer increases substantially. However, evenvery weak negative correlation can drive fetal programmer to extinction when life expectancy is high. Selection for fetal programming,therefore, must be driven by factors that produce significant positive birth-time and lifetime correlations.

that selection cannot result in stable polymorphism of thriftyalleles. In the modern human society, there is considerablevariation in the tendency to become obese or diabetic.Therefore, polymorphism with respect to genes predisposingto obesity and type 2 diabetes presumably exists. If there is nonegative frequency dependence or heterozygote advantage,natural selection will be directional, resulting into thefixation of the advantageous genotype. In that case at novalue of pf, the thrifty and nonthrifty genes can coexist stably.Theoretically, if a heterozygote of thrifty and nonthriftyalleles gets a dual advantage by expressing the right allelein the right environment, the two alleles can coexist, and

the population at any given time will consist of thrifty,nonthrifty, and all time fit individuals. However, there is noempirical evidence of heterozygote advantage so far. Stablepolymorphism is also possible if there is negative frequency-dependent selection. However, if fitness is decided by climaticconditions as assumed by the popular version of thrifty genehypothesis, frequency dependence is unlikely. Therefore,selection during hunter-gatherer stage does not explain theprevalent polymorphism in predisposition to obesity.

(b) Selection after the beginning of agriculture: chronicstarvation due to famines became more serious and commonwith the beginning of agriculture [7, 8]. Signs of chronic


Table 1: Correlations of annual rainfall to that of the subsequent year, short-term (10 years), and long-term (40 years) cumulative:∗ indicatesignificance of individual correlations at α = 0.05 level. Since a large number of statistical tests are being performed, a Bonferroni correctionto the significance level is applicable. After the Bonferroni correction, none of the correlations are significant.

S. No. Subdivision 1 year 10 years cumulative 40 years cumulative

1 Assam Meghalaya 0.047 0.042 −0.0292

Nagaland ManipurMizoram and Tripura

0.006 0.010 −0.078

3Sub-Himalayan WestBengal

−0.061 −0.103 −0.0234 Gangetic West Bengal −0.009 0.096 −0.265∗5 Orissa −0.111 0.135 −0.0966 Jharkhand −0.054 −0.042 −0.0127 Bihar 0.070 −0.158 −0.0488 East Uttar Pradesh 0.096 −0.106 −0.0919 West Uttar Pradesh Plains −0.036 0.010 −0.06610 Haryana −0.010 0.050 0.04111 Punjab −0.050 0.099 0.08112 Rajasthan 0.048 −0.200∗ −0.09413 East Rajasthan 0.047 −0.034 −0.01714 West Madhya Pradesh 0.052 0.114 0.010

15 East Madhya Pradesh −0.076 0.040 −0.03416 Gujarat −0.007 −0.121 −0.11017 Saurashtra and Kutch −0.037 −0.118 −0.15218 Konkan and Goa 0.121 0.154 0.080

19 Madhya Maharashtra 0.227∗ 0.010 −0.16320 Marathwada 0.171 0.043 −0.10121 Vidarbha −0.079 0.026 −0.193∗22 Chhattisgarh 0.039 0.278∗ 0.010

23 Coastal Andhra Pradesh 0.082 0.023 −0.05324 Telangana 0.141 0.110 0.086

25 Rayalaseema −0.104 −0.008 −0.05426 Tamil Nadu 0.049 −0.139 −0.16927 Coastal Karnataka −0.035 −0.042 0.07928 North Interior Karnataka 0.132 0.086 −0.02029 South Interior Karnataka −0.025 −0.087 −0.202∗30 Kerala 0.117 0.083 0.012

starvation on teeth, such as linear enamel hypoplasia, aremore common in early agricultural societies than in hunter-gatherer societies [32]. This is owing to the fact that crops arehighly seasonal in nature, and the failure of a single crop leadsto long-term food scarcity. Such long-term food shortagesare much less probable in hunter-gatherer life, particularly inbiodiversity rich areas. Therefore, if selection for thriftinessstarted acting after the beginning of agriculture, there couldbe transient polymorphism. A testable prediction of thehypothesis would then be that ethnic groups that took toagriculture earlier should show higher tendencies to becomeobese and diabetic. This has not been rigorously testedwith quantitative data. However, ethnic groups, such asthe Australian aborigines, remained hunter gatherers untilrecently, and the recently urbanized individuals of this

community developed a high prevalence of diabetes andhypertension [33]. It is difficult to argue, therefore, thatthrifty genes evolved after the advent of agriculture.

(c) Selection in modern times: intensive agricultural andindustrial societies are modern phenomena not more thana few hundred years old, and it is highly unlikely that thisperiod could have brought about an evolutionary change. Itcan be seen from all the three possible scenarios that naturalselection for the hypothetical thrifty gene(s) is unable toexplain the polymorphism observed today.

Since different geographic regions of the world differin the climatic conditions, seasonality, and food availabil-ity, ethnic groups evolved in different areas should showdifferential predisposition to obesity and related disorders.People evolved in arid or drought-prone areas could have


suffered more frequent famines and, therefore, should havea greater tendency to develop obesity. Also, ethnic groupsfrom harsh winter environments were unable to hunt, fish, orfarm during the colder months and, thus, historically couldhave faced regular feast and famine conditions. Therefore,we may expect higher diabetic tendencies in them. Althoughsubstantial cross-ethnic differences are observed, the trendsare not as expected by the thriftiness hypotheses. Peoplefrom Caucasoid, Eskimo, and some of the Himalayan ethnicgroups that have faced harsh winter environments have aconsiderably lower frequency of obesity and/or T2D [34–38] whereas almost all ethnic groups of warmer habitatshave a high frequency on adopting a Western urban lifestyle[33, 39], contrary to the expectation.

The thrifty phenotype or fetal programming hypothesissuffers from a different set of problems. Fetal programmingcan offer two types of potential advantages. Short-termsurvival advantages in fetal and early infant life and long-term predictive adaptive responses. Our model incorporatesboth of the advantages separately. If the advantage is ofa short duration, it is difficult to explain why a lifetimecommitment to a particular metabolic state could haveevolved. A number of developmental genes have age-specificexpressions, and, therefore, any rigid lifelong programmingfor short-term advantage is a difficult proposition. If climaticfluctuations were the main selective force, it should evolvemetabolic flexibility rather than lifelong rigid programming.Metabolic programming of a lifelong duration based onintrauterine conditions is unlikely to offer a fitness advantageexcept in two sets of conditions. As the model suggests, ifa species has a very short life span, fetal programming foradapting to the birth year conditions can be beneficial, sincethe birth year itself is a substantial part of the total life span.Assuming one year to be the natural unit of seasonal cycles,species with a life span of less than 3–5 years can be expectedto evolve lifelong fetal programming for thriftiness eventhough the adaptive advantage is of a short duration. Forlong-lived species, fetal programming is unlikely to evolveunless there is a significant positive correlation between birthconditions and lifelong conditions. Since such correlationsare not seen in climatological data, there are problems inexplaining evolution of lifelong thrifty programming.

Since the thriftiness hypotheses are inadequate or weakon several grounds, there is a need to rethink the paradigmand consider alternative hypotheses seriously. A detailedcomparative analysis of all the alternative hypotheses isbeyond the scope of this paper. Most of them are new, andall their implications have not yet come forward. We willonly briefly evaluate the alternative hypotheses below to seewhether they offer better explanations where the thriftinesshypotheses are weaker.

(1) Polymorphism: polymorphism in the predispositionto obesity can be potentially explained in three different ways.It is possible that the allelic composition in the populationis close to equilibrium, and there is stable polymorphism.Out of all alternative hypotheses, only the Watve-Yajnikhypothesis is able to predict stable polymorphism since there

is negative frequency-dependent selection in a Hawk andDove like game [42]. An alternative view is that the humanpopulation today is not at a stable equilibrium proportionof alleles but is undergoing drift [8] or selection [21].These alternative hypotheses try to explain polymorphismas a transient state. Speakman [7] also tries to quantifythe drift dynamics; however, his calculation is based onthe assumption that human ancestors became free frompredators about 1.8 million years ago, and this estimate isdebatable [41]. Moreover, if the drift is an ongoing process,it would take a different direction in different populationsand the cross-ethnic differences are expected to be random.Instead, we see that almost all tropical ethnic groups havehigh tendency to develop obesity and T2D on urbanization,and such a generalizable pattern is not expected by thedrift hypothesis. Also, as we know now, a large number ofgenes are associated with obesity, and if drift has to operateindependently on all the genes, it is highly unlikely that it willresult in a directional change.

Moalem et al. [22] presumed that selective pressureschanged substantially by 1800 AD. It has not been criticallyquestioned whether only about 200 years of selection issufficient to generate the current levels of polymorphism.Any data to test the dynamics of both the hypotheses using apredictive model are currently absent.

The third possible explanation to the apparent poly-morphism is that there is no real genetic polymorphism,but individuals are programmed differently depending uponenvironmental conditions faced in early life. Apart from thethrifty phenotype hypothesis, only the Watve and Yajnik[10] hypothesis accounts for lifetime programming in a waydiscussed below.

The cold adaptation hypothesis of Hattersley and Tooke[23] does not explain polymorphism at any of the abovelevels. If high blood glucose is adaptive in cold environ-ments, then ethnic groups who evolved in cold climatesshould undergo directional selection leading to fixation.Polymorphism in that case can only arise by cross-breedingbetween ethnic groups coming from warmer and colderenvironments.

(2) Low birth weight effects: there is globally consistentand strong evidence that a small birth weight is associatedwith altered metabolic and endocrine states in adult life [3,41, 42]. Different interpretations of the birth weight effects[23] have been offered, and the birth weight associationitself cannot be considered a convincing evidence of fetalprogramming. However, rat experiments with maternalnutrient deficiencies have also given strong evidence insupport of fetal programming [43]. Therefore, some fetalprogramming can be safely inferred. It can neverthelessbe debated whether the programming is for thriftiness orfor any other adaptation. Speakman’s [7, 8] drifty genehypothesis, Corbett et al. [21] hypothesis of reverse selectionin modern times, and Moalem et al.’s [22] cold adaptationhypotheses are unable to explain the birth weight effect. Wells[44] and Watve and Yajnik [10] have different interpretationsof the effect of maternal nutrient limitation, but both of themadequately account for birth weight effects. Wells’ hypothesis[44] of maternal advantage does not have a predictive


adaptation element and, therefore, does not explain lifetimerigidity in the programming. Watve and Yajnik [10] arguethat fetal undernourishment affects muscle mass more thanbrain mass and, therefore, a brain-dependent “diplomat”lifestyle marked by insulin resistance is adaptive for lowbirth weight individuals. Since muscle cells do not replicatein adult life, this early developmental limitation persiststhroughout life, and, thus, lifetime programming couldevolve. At the same time, low birth weight is not a necessaryprerequisite for the Watve and Yajnik hypothesis to work.Any hypothesis exclusively based on fetal programmingsuffers from another problem in that although low birthweight individuals have a greater probability of developingobesity and T2D, a large proportion of adult type 2 diabeticsare not born with low weights. Therefore, any hypothesisexclusively dependent upon fetal conditions are inherentlyinadequate.

(3) Birth time versus lifetime correlation: the modelpredicts that for lifetime fetal programming to evolve,a positive correlation between intrauterine and lifetimeconditions is necessary. Although climatic variations areunlikely to cause such correlations, there can be other causesof food deprivation apart from climatic factors. A highpopulation density can lead to increased competition, result-ing into undernourishment. Although population densitymay oscillate, the oscillations typically span over severalgenerations so that an individual born at a high populationdensity is expected to see high population density throughmost of his life. Therefore, periodic food scarcity caused bypopulation oscillations can result into positive correlationsbetween birth year and lifetime nutritional conditions. Insocial species, the social hierarchy might also be relatedto differential access to food. An individual born smallerand weaker is more likely to face social subordination,and, therefore, an anticipatory fetal programming would beadaptive. These social causes can produce positive birth-lifetime correlations, and they are more likely candidates toselect for fetal programming of lifelong duration. There exista broad range of metabolic, endocrinological, behavioral,and cognitive adaptations that accompany social hierarchicalpositions. Therefore, if fetal programming is in response tosocial hierarchies, we expect that it need not be restricted todiet and energy homeostasis, but it affects a large numberof systems of the body along with brain and behavior. Thisindeed, is the case, and type 2 diabetes involves almost allsystems of the body [10, 45]. Social subordination is animportant predisposing factor for type 2 diabetes [46], andthe link between the two is elaborately explained by Belsareet al. [40].

We suggest that on exposure to intrauterine malnourish-ment, there is fetal programming in anticipation of physicalweakness and social subordination or anticipation of highpopulation density rather than anticipation of famine. Inprimate societies, it is known that social ranks of juvenilesand subadults are influenced by the ranks of their mothers[47, 48]. If social rank influences preferential access to foodresources, it is sufficient to produce the positive correlationbetween fetal and lifetime nutritional conditions needed forthe evolution of fetal programming. It appears logical, there-

fore, that fetal programming could have evolved inanticipation of population- and social hierarchy-relatedfactors than anticipation of famines or other climate-relatedfactors.

(4) Multiple effects: obesity and T2D are not only aboutenergy homeostasis but also about changes in innate immu-nity, sexual and reproductive function, vascular developmentand function, skin architecture, wound healing and tissueregeneration, memory, cognitive functions, behavior andmechanisms of decision making, social relations, and socialsignaling. Therefore, any hypothesis about the origins ofobesity needs to account for all the network of changesadequately. Most hypotheses are too glucolipo-centric and,therefore, fall short of this criterion. The only possibleexception is the Watve-Yajnik hypothesis which has aninherent expectation that a number of systems of the bodywould be simultaneously involved [10, 40].

Implications for genetics of obesity: a large body ofresearch has now focused on the genetics of obesity. Anincreasing number of loci and mutants associated withobesity are being identified. However, there are certaininternal paradoxes associated with these data. Studies priorto the genomic era that were based on familial, twin-pair,and adoption studies typically predicted a large heritablecomponent in obesity (reviewed by [49]). The genome-wideassociation studies, on the other hand, have identified a largenumber of associations, but together they explain a verysmall fraction of variance in obesity parameters [50–54],leaving a large gap between the pregenomic and emerginggenomic picture. We are yet to understand the reasons forthis discrepancy, but a most likely implication goes against allhypotheses that assume a gene or a set of genes for obesity.These hypotheses include Neel’s thrifty gene, Speakman’sdrifty gene, Corbett et al.’s reverse selection, and Moalem etal.’s cold adaptation. On the other hand, fetal programminghas a promise for filling the gap between the familial studiesand genome-wide association studies. The programming islikely to involve epigenetic mechanisms as well. Three of theabove hypotheses involve fetal programming, namely, theclassical thrifty phenotype hypothesis, maternal adaptationhypothesis by Wells [44], and behavioural switch hypothesisby Watve and Yajnik [10]. Since our model has indicatedthe inadequacies of the thrifty phenotype hypothesis, theother two need to be considered more seriously. We suggest,here, that more than one type of metabolic programmingmay be involved in obesity, and they may be inducedin various stages of life. There are strong data for fetalprogramming, but behavioral programming is also likelyto affect metabolism, since associations between neuroen-docrine mechanisms of behavior and metabolic states areknown [40].

The classical thriftiness family of hypotheses wouldexpect genes associated with metabolic rates to be the obesitygenes. The behavioural origins hypothesis, on the otherhand, expects genes involved in sexual function, cognitiveabilities, immunity, regulation of aggression, and otherbehavioral traits to be associated with obesity and relateddisorders. It would be useful to interpret the emergingdata on genome-wide associations and, perhaps, near future


studies on epigenetics of obesity in the light of the morepromising hypotheses from the above.

We have shown with a simple theoretical model that theclassical concepts of thrifty gene as well as fetal programmingfor thriftiness face a number of problems and are inadequateto account for the observable trends in the modern epidemicof obesity and related disorders. A number of alternativehypotheses have been suggested recently. It might be tooearly to discard any of the hypotheses right away, but acomparative analysis shows the strengths and weaknessesof each of them. Since search for obesity-associated geneshas so far given limited success in terms of explainingpopulation variance in obesity parameters, the hypothesesinvolving phenotypic programming look more promising.In understanding obesity and related disorders, the needfor a combined genomic-phenomic approach is increasinglybeing felt [55, 56], and such an integration can be bestachieved on the platform of an evolutionary insight intothe phenomenon. Greater efforts are, therefore, needed tounderstand the evolutionary background of obesity andrelated pathophysiological conditions.

Acknowledgments

P. Belsare was supported by CSIR during the study. Discus-sions with Sulochana Gadgil (IISc) and Ashwini Kulkarni(IITM) were useful.

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CanThriftyGene(s)orPredictiveFetalProgrammingfor ...thriftiness of low birth weight individuals [13, 14]. Studies using doubly labeled water have not consis-tently found lower metabolic